Journal of Environmental Radioactivity 213 (2020) 106104
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Refinement of source term and atmospheric dispersion simulations of radionuclides during the Fukushima Daiichi Nuclear Power Station accident Hiroaki Terada a, *, Haruyasu Nagai a, Katsunori Tsuduki a, Akiko Furuno a, Masanao Kadowaki a, Toyokazu Kakefuda a, b a b
Japan Atomic Energy Agency, 2-4 Shirakata, Tokai, Ibaraki, 319-1195, Japan KCS Corp., 1-4 Yazucho, Mito, Ibaraki, 311-4196, Japan
A R T I C L E I N F O
A B S T R A C T
Keywords: Fukushima Daiichi nuclear power station accident Source term Atmospheric dispersion WSPEEDI Database for dose assessment
To assess the radiological dose to the public resulting from the Fukushima Daiichi Nuclear Power Station (FDNPS) accident in Japan, especially for the early phase of the accident when no measured data are available for that purpose, the spatial and temporal distributions of radioactive materials in the environment need to be reconstructed through computer simulations using the atmospheric transport, dispersion, and deposition model (ATDM). For the ATDM simulation, the source term of radioactive materials discharged into the atmosphere is essential and has been estimated in many studies. In the present study, we further refined the source term estimated in our previous study and improved the ATDM simulation with an optimization method based on Bayesian inference, which used various measurements such as air concentration, surface deposition, fallout, and newly released hourly air concentrations of 137Cs derived by analyzing suspended particulate matter (SPM) collected at air pollution monitoring stations. This optimization improved not only the source term but also the wind field in meteorological calculation, which led to the reduction of discrepancies in plume passage time at monitoring points to less than 3 h between calculations and measurements, by feeding back comparison results between the dispersion calculations and measurements of radionuclides. As a result, the total amounts of 137Cs and 131I by the present study became 1.0 � 1016 and 1.2 � 1017 Bq, respectively, and decreased by 29% and 20%, respectively, in comparison with those by previous study. The ATDM simulation successfully reproduced both the air concentrations at SPM monitoring points and surface depositions by airborne monitoring. FA10 for total samples of air concentrations of 137Cs at SPM monitoring points increased from 35.9% by the previous study to 47.3%. The deposition amount on the land decreased from 3.7 � 1015 Bq by the previous study to 2.1 � 1015 Bq, which was close to the measured amount of 2.4 � 1015 Bq. We also constructed the spatiotemporal distribution of some major radionuclides in the air and on the surface (optimized dispersion database) by using the optimized release rates and ATDM simulations. The optimized dispersion database can be used for comprehensive dose assessment in tandem with behavioral patterns of evacuees from the FDNPS accident by collaborating research group in the Japanese dose assessment project. The improvements in the present study lead to the refinement of the dose estimation.
1. Introduction The Fukushima Daiichi Nuclear Power Station (FDNPS) accident in Japan, which was triggered by a magnitude 9.0 earthquake and result ing tsunami on March 11, 2011, caused a month-long discharge of sig nificant amount of radioactive materials into the atmosphere. To assess the radiological dose to the public caused by this release, several issues
must be resolved. The internal dose due to short-lived radionuclides, such as radioiodine, during the early phase of the accident cannot be assessed by using measured data. Additionally, the external dose due to direct radiation from the radioactive plume and ground shine at the point without measurement were considered to have considerable temporal variation due to several plume passages. To address these problems, the atmospheric transport, dispersion, and deposition model
* Corresponding author. 2-4 Shirakata, Tokai, Ibaraki, 319-1195, Japan. E-mail address:
[email protected] (H. Terada). https://doi.org/10.1016/j.jenvrad.2019.106104 Received 30 May 2019; Received in revised form 8 November 2019; Accepted 8 November 2019 Available online 16 December 2019 0265-931X/© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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(ATDM) simulation may be employed to reproduce the spatial and temporal distribution of radioactive materials. By simulating atmo spheric dispersion conditions that are consistent with actual measure ments, the calculation results can complement spatiotemporally discrete measurements for dose assessment. The source term of radioactive materials released into the atmo sphere during the FDNPS accident is essential for evaluating the envi ronmental distribution of radioactive materials. We have estimated the source term by comparing measurements of air concentration of radio active materials or dose rate in the environment with calculated results using ATDM [Chino et al., 2011; Katata et al., 2012a, 2012b; Terada et al., 2012]. The ATDM used in these studies were the System for Prediction of Environmental Emergency Dose Information (SPEEDI) operated by the Ministry of Education, Culture, Sport, Science and Technology (MEXT) and the Worldwide version of SPEEDI (WSPEEDI) [Terada and Chino 2008] developed by the Japan Atomic Energy Agency (JAEA). Our source term has been validated through comparison with other estimations or its use in simulations of ATDMs [Terada et al., 2012; Morino et al., 2013; Draxler et al., 2015]. The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) has used our source term [Terada et al., 2012] for estimating levels of radioactive material in the terrestrial environment and doses to the public, as they were derived from measurements of radioactive material in the environment using the ATDM model optimized to fit the mea surements [UNSCEAR 2014]. However, UNSCEAR also pointed out in its 2013 report that the source term has large uncertainties and must be improved in future studies, in particular as more information becomes available on the progression of the accident, greater use is made of measurements in the environment, and improved assessment methods are implemented [UNSCEAR 2014]. To improve our source term, we made detailed estimation by using additional monitoring data near the FDNPS and conducting simulation using WSPEEDI, which was modified with a new deposition scheme [Katata et al., 2015]. The modified WSPEEDI simulation and the latest source term successfully reproduced the local and regional deposition patterns of 131I and 137Cs derived from airborne monitoring by the United States Department of Energy (US-DOE) and MEXT [MEXT 2012; Torii et al., 2013]. For further improvement, we attempted to use 134 Cs/137Cs ratios of inventories in Units 1 to 3 of FDNPS and those in surface depositions [Chino et al., 2016]. The variation in 134Cs/137Cs ratios in surface depositions of environmental samples [Mikami et al., 2015] was analyzed through comparison with those of inventories in FDNPS reactors [Nishihara et al., 2012]. As a result, the source term was also revised for a section of the release period. In the present study, the updated version of WSPEEDI, which has been further improved, was used to reconstruct the spatial and temporal distribution of radioactive materials in the environment during the FDNPS accident. A database for the spatiotemporal distribution of radioactive materials in the air and on the surface (dispersion database) was also developed from the output of the ATDM simulations, which is used in the Japanese dose assessment project. To enhance the repro ducibility of dispersion processes for dose estimation, the source term and ATDM simulations were optimized using the Bayesian inference method. In this optimization, new monitoring data including hourly air concentration of 137Cs derived from suspended particulate matter (SPM) collected at air pollution monitoring stations [Oura et al., 2015; Tsuruta et al., 2018] were additionally used. In the application of this kind of objective statistical analysis to the local-scale atmospheric dispersion simulations in the present study, the reproducibility of the meteorological field should be taken into consid eration. Although there were studies to estimate the source term during the FDNPS accident by using Bayesian inference method with globalscale atmospheric dispersion simulations and air concentration dataset [Stohl et al., 2012; Maki 2015], studies of this kind using local-scale simulations and dataset, especially the SPM data, have not been car ried out. Considering the narrow width of radioactive plume discharged
from a point source in the local-scale atmospheric dispersion, a slight difference in plume flow direction and passage time caused by the wind field discrepancy in meteorological calculations may result in a large difference of air concentration at monitoring points, and consequently in significant errors in the source term estimation. Therefore, the correc tion of this discrepancy by “expert judgment” based on the knowledges and experiences is necessary to reduce the impact of model uncertainty on the source term estimation in our previous studies [Chino et al., 2011; Katata et al., 2012b; Katata et al., 2015]. In the present study, a com bination of ensemble meteorological calculations and the Bayesian inference method is applied to improve the reproducibility of meteo rological field by feeding back comparison results between the disper sion calculations and measurements of radionuclides. This new method enables the source term estimation based on the objective statistical analysis without “expert judgment”. The optimized dispersion database was used for the comprehensive dose assessment along with the behavioral pattern of evacuees from the FDNPS accident by collaborating research group in the Japanese dose assessment project [Ohba et al., 2019]. The database can also provide basic information and simulation techniques to understand the envi ronmental impacts of the accident and to predict future conditions. 2. Materials and methods 2.1. ATDM and its improvement The ATDM used in the present study is WSPEEDI [Terada and Chino 2008] developed by JAEA. The original WSPEEDI consists of the non-hydrostatic mesoscale atmospheric model (MM5) [Grell et al., 1994] and the Lagrangian particle dispersion model (GEARN) [Terada and Chino 2008]. MM5 is a community model with many users all over the world, while GEARN calculates the atmospheric dispersion of ra dionuclides by tracing the trajectories of a large number (typically a million) of marker particles discharged from a release point. The hori zontal model coordinates are the map coordinates, while the vertical coordinate is the terrain-following coordinate (z*-coordinate). Using the meteorological field predicted by MM5, GEARN calculates the move ment of each particle affected by both the advection due to mean wind and subgrid-scale turbulent eddy diffusion. GEARN has a function of nesting calculations for two domains corresponding to MM5’s nested domains. Two nested domains of GEARN are calculated concurrently by different executables on parallel computers, and marker particles that flow out and in across the boundary of the inner domain are exchanged between domains. A part of the radioactivity in the air is deposited on the ground surface by turbulence (dry deposition) and precipitation (wet deposition). The meteorological model has been improved in the present study by introducing a new meteorological model (WRF) [Skamarock et al., 2008] and a data assimilation method (WRF-DA) [Barker et al., 2012]. WRF is the updated model of MM5 and has many new physical options and functions including the advanced data assimilation method WRF-DA. Note that WRF version 3.6.1 is used in the present study. GEARN was modified to use a sophisticated deposition scheme in a previous study [Katata et al., 2015]. The new scheme addresses dry and fog-water deposition, cloud condensation nuclei activation, and subse quent wet scavenging due to mixed-phase cloud microphysics (in-cloud and below-cloud scavenging) for radioactive iodine gas (I2 and CH3I) and other particles (CsI, Cs, and Te) [Katata et al., 2015]. The simulation settings are mostly consistent with those by Katata et al. (2015). For WRF and WRF-DA, descriptions of the calculation conditions can be read in another paper [Kadowaki et al., 2017]. The calculation domain setting was changed in the present study from the previous study, as shown in Fig. 1. The nesting domain 2 of the WRF calculation, which is the outer domain of the GEARN nesting calcula tions, was expanded eastward to cover a larger area over the Pacific Ocean. Other calculation settings are summarized in Table 1. 2
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Journal of Environmental Radioactivity 213 (2020) 106104
Fig. 1. Computational domains for the atmospheric dispersion simulations. Domain 2 in this study (b) was expanded eastward to cover a larger area over the Pacific Ocean than that of previous studies (a) [Katata et al., 2015; Kadowaki et al., 2017]. Colored shades indicate the topography height. The numbers in (a) indicate the prefectures listed in the right table.
2.2. Development of a dispersion database from ATDM calculations A database of the spatiotemporal distribution of radioactive mate rials in the air and on the surface (dispersion database) is constructed with a newly developed calculation method [Terada et al., 2017] as shown in Fig. 2. At first, meteorological calculations are carried out using WRF to make a dataset of meteorological fields. Then, the dispersion calculation using GEARN is conducted with the unit release condition for a 1-h time segment of the release period. This calculation is performed for each combination of the meteorological field, five representative radionuclides for the deposition property (noble gas, particulate iodine, organic and inorganic iodine gases, and other parti cles) without decay, release heights (20 m and 120 m above the ground, volume sources with 100 � 100 � 100 m3 and 100 � 100 � 300 m3 for hydrogen explosions at Units 1 and 3, respectively), and 480 release time segments (all 24 h from 00:00 JST (Japan Standard Time) on March 12 to 00:00 JST on April 1, 2011); matrix outputs for every calculation condition are made. In the dispersion database, the spatiotemporal distribution of air concentration (Ct,i,j,k,n (Bq m–3)) and surface deposi tion (Dt,i,j,n (Bq m–2)) of radionuclides for any condition of the source term can be reproduced immediately by a linear combination of matrix outputs using
Fig. 2. Calculation and analysis method for database.
Table 1 Calculation settings of WRF and GEARN. Calculation domain
Domain 1
Data assimilation by WRF-DA Applied GEARN calculation Grid number of WRF Grid number of GEARN Horizontal grid resolution Time step of WRF Time step of GEARN Boundary and initial conditions of WRF Physics option of WRF Microphysics Cumulus Land surface Boundary layer Radiation
Yes No No No Yes Yes 100 � 100 � 31 190 � 190 � 31 190 � 190 � 31 – 188 � 188 � 29 188 � 188 � 29 9 km 3 km 1 km 45 s 15 s 5s – 12 s 4s Grid Point Value (GPV) of Meso-Scale Model (MSM) by Japan Meteorological Agency (JMA)
Domain 2
Domain 3
WSM6 [Hong and Lim 2006] Betts–Miller–Janjic [Janjic 1994] No Five-layer thermal diffusion [Skamarock et al., 2008] Mellor–Yamada–Nakanishi–Niino (MYNN) Level 2.5 [Nakanishi and Niino 2004] Long wave: Rapid Radiative Transfer Model (RRTM) [Mlawer et al., 1997], Short wave: Dudhia [Dudhia 1989]
3
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XX Ct;i;j;k;n ¼ fdn;t XX Dt;i;j;n ¼ fdn;t h
� Cdbr;h;t;i;j;k;mðnÞ Rr;h;n ;
Table 2 Spatiotemporal characteristics of monitoring data (air concentration from dust sampling (Dust), air dose rate from airborne monitoring, monitoring post (MP), and monitoring car (MC), deposition from airborne monitoring, marine, and field observations, and fallout at monitoring station) and their usage in the source term estimation by previous studies and optimization analysis in the present study.
(1)
r
h
� Ddbr;h;t;i;j;mðnÞ Rr;h;n ;
(2)
r
where fdn,t is the decay rate for a radionuclide (n) at the output time (t) from the shutdown time; Cdbr,h,t,i,j,k,m(n) and Ddbr,h,t,i,j,m(n) are matrix outputs for air concentration and surface deposition, respectively, for release segment (r), release height (h), output time (t), and representa tive radionuclide (m(n)) at grid point (horizontal: i, j, vertical: k); and Rr, h,n is the release rate decay corrected at the shutdown time for release segment (r), release height (h), and radionuclide (n). It is easy to compare results applying many kinds of source term with monitoring data; thus allows us to find out the optimum source term with which the difference between the calculations and measurements can be mini mized. This method can also be applied to select the optimum meteo rological field from many meteorological calculations with different calculation conditions.
Data [ref.]
2.3. Measurement data A variety of environmental monitoring data were used in our pre vious source term estimations [Chino et al., 2011; Katata et al., 2012a, 2012b; Terada et al., 2012; Katata et al., 2015; Chino et al., 2016]. Following these studies, new monitoring data of hourly air concentra tions of 137Cs derived by analyzing SPM collected at air pollution monitoring stations [Oura et al., 2015; Tsuruta et al., 2018] were released. Analysis of body surface contamination levels [Ohba et al., 2017] provided information about radionuclide composition during the early phase of the accident. These new data were added for the source term estimation in the present study. Spatiotemporal characteristics of these data and their usage in the previous source term estimation are summarized in Table 2. The air concentration data from dust samplings [MEXT 2011a; Amano et al., 2012; Ohkura et al., 2012] were primarily used in the first source term estimation [Chino et al., 2011], considering the accuracy of estimation using the ratio of measured air concentration to calculated air concentration of each nuclide at the sampling points (method 1). Air dose rates data around the northwest direction of FDNPS [MEXT 2011b] were also used for the estimation by comparing observed spatial pat terns of air dose rates from radionuclides on the ground surface (i.e., ground-shines) with calculated air dose rates (method 2), as there were no other data available to estimate the most significant release, which caused the high air dose rate zone around the northwest direction of FDNPS at that time. The air dose rates data around the FDNPS [METI 2011; Fukushima Prefecture 2012] were also used for the reverse esti mation with method 2 in the following source term estimations [Katata et al., 2012b, 2015]. As additional data for the source term estimation, the air dose rate data were used by assuming the composition of major radionuclides, which were derived from dust sampling data [MEXT 2011a; Amano et al., 2012; Ohkura et al., 2012; TEPCO 2011b; Furuta et al., 2011; Yamada et al., 2013; KEK 2011; Tokyo Metropolitan Gov ernment 2011]. Other data, such as the air dose rate at monitoring posts far from FDNPS [Fukushima Prefecture 2011a, 2011b; TEPCO 2011a; Ibaraki Prefecture 2011a, 2011b; JAEA 2011; Tochigi Prefecture, 2011] and airborne monitoring [MEXT 2011c; Sanada and Torii 2015], the surface deposition map generated by the airborne monitoring [MEXT 2012; Torii et al., 2013], the daily fallout data [MEXT 2011d], and global scale air concentration measurement by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) [CTBTO 2011], were used as supplements for the detailed analysis to validate the estimated source term by comparing the measurements with simulation results using the esti mated source term [Terada et al., 2012; Katata et al., 2015]. However, the source term for the period when the plume flowed toward the Pacific
Characteristic (space/ time)
Usage [ref.]
Air concentration data Dust in Fukushima [1] Dust at JCAC [2] Dust at JAEA-1 [3] Dust near FDNPS [4] Dust at FD2NPSa [5] Dust at JAEA-2 [6]
Points/specific time A point/time series A point/time series Points/specific time A point/daily A point/time series
Dust at JAEA-3 [7]
A point/time series
Dust at KEK [8]
A point/daily
Dust in Tokyo [9] CTBTO data [10] SPM data [25] Air dose rate data MC out of 20 km zone [11] Airborne in 80 km area [12] MP in Fukushima [13] MP at FD2NPS [14] MP in Ibaraki [15] MP at JAEA [16] MP in Tochigi [17] MC near FDNPS [4] MP near FDNPS [18] Airborne in 5 km area [19] Deposition data Airborne in East Japan [20] Airborne in early time [21] Marine obs. [22] Field obs. [23] Fallout data Fallout data [24]
A point/time series Points/time series Points/time series
Method 1 [a, e], this study Method 1 [a, e], this study Method 1 [a, e], this study Method 1 [c, e], this study Method 1 [d, e], this study 131 137 I/ Cs ratio [d, e], this study 131 137 I/ Cs ratio [d, e], this study 131 137 I/ Cs ratio [d, e], this study 131 137 I/ Cs ratio [d, e] Comparison [e] This study
Points/specific time
Method 2 [a]
Map/specific time
Comparison [b]
Points/time series Points/time series Points/time series A point/time series Points/time series Points/specific time Points/time series Map/specific time
Comparison [b, c, e] Comparison [b, c, e] Comparison [b, c, e] Comparison [b, c, e] Comparison [b, e] Method 2 [c, e] Method 2 [e] Comparison [e]
Map/specific time
Comparison [d, e], this study Comparison [e], this study
Map/specific time Map/specific time Map/specific time Points/daily
Correction [9] Cs/137Cs ratio [f]
134
Comparison [d, e], this study
Reference: [1] MEXT 2011a, [2] Amano et al., (2012), [3] Ohkura et al., (2012), [4] METI 2011, [5] TEPCO 2011b, [6] Furuta et al., (2011), [7] Yamada et al., (2013), [8] KEK 2011, [9] Tokyo Metropolitan Government (2011), [10] CTBTO 2011, [11] MEXT 2011b, [12] MEXT 2011c, [13] Fukushima Prefecture (2011a); 2011b, [14] TEPCO 2011a, [15] Ibaraki Prefecture (2011a); 2011b, [16] JAEA 2011, [17] Tochigi Prefecture (2011), [18] Fukushima Prefecture (2012), [19] Sanada and Torii (2015), [20] MEXT 2012, [21] Torii et al., (2013), [22] Honda et al., (2012); Aoyama et al., (2013), [23] Mikami et al., (2015), [24] MEXT 2011d, [25] Oura et al., (2015); Tsuruta et al., (2018), [a] Chino et al., (2011), [b] Katata et al., (2012a), [c] Katata et al., (2012b), [d] Terada et al., (2012), [e] Katata et al., (2015), [f] Chino et al., (2016). a FD2NPS: Fukushima Daini nuclear power station.
Ocean had a large uncertainty because there were no data over the ocean and the source term was estimated by interpolation or extrapolation of values from other periods. The concentration data of 134Cs and 137Cs in sea surface water [Honda et al., 2012; Aoyama et al., 2013] were used for modification of the source term in those periods by detailed source term estimation [Kobayashi et al., 2013; Katata et al., 2015]. Further improvement of the source term is expected by using hourly air concentration of 137Cs from the SPM data [Oura et al., 2015; Tsuruta et al., 2018]. These data have spatially and temporally high resolution for reproducing the air concentration of radionuclides during the plume passage. An objective analysis using these data is applied in the present study to optimize the source term and ATDM simulations. 4
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2.4. Optimization method
Table 3 Divided segments of meteorological calculation and selected ensemble case.
To optimize the source term and ATDM simulations by comparing calculation results with monitoring data, a new method with combina tion of ensemble meteorological calculations and statistical analysis method was developed. The optimization analysis was carried out in two steps: optimization of the meteorological calculation to improve the reproducibility of meteorological filed by feeding back comparison re sults between the dispersion calculations and measurements of radio nuclides and optimization of the source term by using the optimized meteorological field. The statistical analysis method and optimization methods for two optimization steps are described in the following sections.
d)TC(d) 1(Ms
s0)TC(s0) 1(s
d) þ (s
s0)]/2,
(3)
where M is the source receptor matrix for hourly release segments, s0 is the prior release rate vector, C(x) is the uncertainty covariance matrix for vector x [ ¼ σx(i)2δ(i,j)], and δ is the Kronecker delta. The solution for s to minimize J is given by Tarantola (1987) as follows: s ¼ s0 þ [MTC(d)
1
M þ C(s0)
1
]
1
MTC(d)
1
(d
Ms0).
Period (JST)
Selected ensemble case
1 2 3 4 5 6
3/11 21:00 to 3/14 3:00 (2 days þ 6 h) 3/14 3:00 to 3/17 3:00 (3 days) 3/17 3:00 to 3/20 3:00 (3 days) 3/20 3:00 to 3/24 3:00 (4 days) 3/24 3:00 to 3/28 3:00 (4 days) 3/28 3:00 to 4/1 0:00 (3 days þ 21 h)
þ72 min case Control case þ72 min case Control case Control case 48 min case
air concentration and deposition of radionuclides using uncertain in formation of radionuclides composition. The components in the uncertainty covariance matrix for the moni toring data, C(d), were set separately for data type based on their standard deviations. For the SPM data, the standard deviation was calculated at each measured point. One standard deviation value was used for each other data type (deposition: 300,000 Bq m 2, fallout: 1,000 Bq m 2, dust sampling: 70 Bq m 3 for 137Cs; deposition: 1,700,000 Bq m 2, fallout: 10,000 Bq m 2, dust sampling: 700 Bq m 3 for 131I). The uncertainty regarding the dispersion calculation was also added to C(d). This uncertainty, which was added to each component of C(d), was calculated for each monitoring point and time using calcu lated values at the grid of the monitoring point and the surrounding four grids. The uncertainty of the calculated value at the monitoring point was obtained from the sum of the gradient values between the grid of the monitoring point and the four points divided by the average value of the five points and multiplied by the component value of C(d) at the monitoring point. It means that the gradient of the calculated horizontal distribution may indicate the uncertainty of the calculation because the large gradient around the monitoring point causes a large discrepancy between calculation and measurement, if the calculated distribution deviates from the actual condition.
2.4.1. Statistical analysis method Here, we used the CO2 emission estimation method [Gurney et al., 2003] based on the Bayesian synthesis method [Enting 2002]. In this method, the hourly release rate vector (s) is estimated from monitoring data vector (d) by minimizing the cost function (J) as follows: J ¼ [(Ms
Segment No.
(4)
For the prior release rate, we used the hourly values generated from our latest source term [Katata et al., 2015; Chino et al., 2016]. The uncertainty covariance matrix for the prior release rate, C(s0), was generated assuming that the release rate at each time segment has 100% uncertainty, as σs0(i) ¼ s0(i). The source receptor matrix, M, can be derived from the dispersion database using equations (1) and (2), as described in section 2.2. The monitoring data vector, d, was produced by sequentially putting all data from spatially or temporally distributed data of air concentration, deposition, and fallout in Table 2. The air dose rate data in Table 2 were not used in this analysis because large un certainties were expected in the estimated air dose rate from calculated
2.4.2. Optimization method for meteorological calculation In the first optimization step, further improvement of meteorological calculation was carried out by applying an ensemble calculation of WRF. We used the ensemble calculation method based on Lagged Average Forecasting [Hoffman and Kalnay 1983]. Initial conditions for 10
Fig. 3. Optimization flow of meteorological calculation using ensemble calculations. 5
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Table 4 Updated source term of137Cs and131I for the period from 05:00 JST on March 12 to 00:00 JST on April 1, 2011. Release rates are decay corrected at the shutdown time of 14:46 JST on March 11, 2011. Release period
Release rate (Bq h
Start time (JST)
Duration (h)
137
3/12 3/12 3/12 3/12 3/12 3/12 3/12 3/13 3/13 3/13 3/13 3/13 3/13 3/14 3/14 3/14 3/14 3/14 3/14 3/14 3/14 3/14 3/14 3/14 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/15 3/16 3/16 3/16 3/16 3/16 3/16 3/16 3/16 3/16 3/17 3/17 3/18 3/18 3/18 3/18 3/18 3/19 3/19 3/20 3/20 3/21 3/21 3/21 3/21 3/22 3/24 3/25 3/26 3/28 3/29 3/30 3/31 3/31
4.0 1.0 4.0 1.0 1.0 6.0 6.0 5.0 3.0 1.0 2.0 8.0 3.0 1.0 4.0 4.0 1.0 6.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 3.0 3.0 1.0 5.0 2.0 2.0 2.0 1.0 1.0 1.0 5.0 3.0 2.0 1.0 1.0 1.0 1.0 15.0 15.0 3.0 5.0 3.0 5.0 5.0 11.0 10.0 19.0 10.0 12.0 4.0 4.0 5.0 26.0 25.0 24.0 35.0 47.0 35.0 14.0 13.0 22.0 2.0
3.88 � 4.20 � 2.70 � 7.20 � 2.70 � 1.40 � 3.23 � 2.15 � 2.60 � 3.80 � 4.95 � 3.00 � 8.20 � 6.30 � 4.40 � 3.50 � 1.86 � 1.80 � 1.10 � 1.00 � 1.00 � 2.90 � 1.20 � 1.25 � 1.30 � 6.60 � 1.00 � 1.40 � 1.40 � 7.77 � 7.90 � 1.06 � 5.15 � 2.60 � 7.20 � 3.40 � 1.90 � 1.27 � 6.64 � 1.83 � 2.60 � 1.20 � 1.50 � 2.90 � 5.00 � 6.20 � 3.10 � 3.00 � 2.10 � 8.15 � 1.98 � 9.71 � 5.80 � 9.90 � 6.48 � 2.18 � 3.70 � 4.38 � 4.10 � 2.80 � 1.07 � 2.24 � 6.31 � 2.20 � 6.91 � 3.00 � 5.87 � 4.32 � 2.70 � 8.91 �
a
5:00 9:00 10:00 14:00 15:00 16:00 22:00 4:00 9:00 12:00 13:00 15:00 23:00 2:00 3:00 7:00 11:00 12:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 7:00 10:00 11:00 16:00 18:00 20:00 22:00 23:00 0:00 1:00 6:00 9:00 11:00 12:00 13:00 14:00 15:00 6:00 21:00 0:00 5:00 8:00 13:00 18:00 5:00 15:00 10:00 20:00 8:00 12:00 16:00 21:00 23:00 0:00 0:00 11:00 10:00 21:00 11:00 0:00 22:00
1
)
Release height (m) 131
Cs
I
1012 1012 1012 1013 1014 1013 1013 1013 1013 1013 1013 1013 1012 1012 1012 1012 1014 1012 1012 1012 1012 1013 1012 1012 1012 1013 1014 1014 1013 1013 1013 1013 1013 1014 1013 1014 1013 1013 1012 1013 1014 1013 1013 1013 1013 1013 1013 1013 1013 1013 1013 1013 1013 1013 1012 1013 1013 1012 1011 1012 1012 1012 1011 1012 1011 1012 1012 1013 1012 1011
Volume source dimension in the directions x, y, and z considering hydrogen explosion.
6
4.05 � 4.50 � 2.96 � 5.70 � 4.20 � 1.19 � 2.31 � 2.22 � 3.04 � 4.48 � 6.40 � 3.61 � 1.01 � 7.81 � 5.49 � 4.43 � 2.30 � 2.33 � 1.44 � 1.32 � 1.40 � 3.50 � 1.60 � 1.80 � 1.80 � 5.20 � 3.00 � 5.50 � 2.00 � 1.23 � 1.30 � 1.54 � 5.45 � 1.99 � 6.80 � 2.10 � 2.90 � 2.80 � 2.66 � 3.23 � 2.70 � 2.10 � 2.29 � 4.44 � 7.69 � 9.47 � 5.08 � 5.10 � 3.60 � 2.10 � 2.88 � 2.89 � 1.50 � 1.91 � 7.69 � 3.37 � 2.25 � 1.03 � 1.18 � 5.10 � 2.92 � 5.74 � 5.13 � 1.26 � 6.46 � 1.21 � 4.26 � 2.66 � 7.95 � 5.30 �
1013 1013 1013 1015 1015 1014 1014 1014 1014 1014 1014 1014 1014 1013 1013 1013 1015 1013 1013 1013 1013 1014 1013 1015 1013 1014 1014 1014 1014 1015 1015 1014 1014 1015 1014 1016 1014 1014 1014 1014 1015 1014 1014 1014 1014 1014 1014 1014 1014 1015 1015 1015 1015 1015 1013 1014 1014 1014 1014 1013 1014 1014 1013 1014 1013 1013 1013 1014 1013 1012
20 20 20 120 100 � 100 � 100a 120 120 120 120 120 120 120 120 120 120 120 100 � 100 � 300a 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20, 120 20, 120 20, 120 20, 120 20, 120 20, 120 20, 120 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
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Table 5 Statistics of comparison, the percentage within a factor of 2, 5, and 10 (FA2, FA5, FA10), logarithmic correlation coefficient (CC), of 6-h average values of137Cs air concentration between calculations (Katata et al., 2015 and this study) and SPM data. Statistics were calculated for fractions of samples within specific area and period regarding the plume passage as well as for the total samples. Higher scores in comparison between Katata et al., (2015) and this study are shown in bold letters. Region and period
Calculation
(1) North of FDNPS, 12 to 14 March
Katata et al. This study Katata et al. This study Katata et al. This study Katata et al. This study Katata et al. This study Katata et al. This study Katata et al. This study
(2) Kanto*, 15 to 16 March (3) Fukushima, 15 to 16 March (4) North of FDNPS, 18 to 19 March (5) Southern Tohoku**, 20 to 21 March (6) Kanto, 20 to 21 March Total
(2015) (2015) (2015) (2015) (2015) (2015) (2015)
FA2 (%)
FA5 (%)
FA10 (%)
CC
12.5 43.8 9.1 28.0 15.2 21.2 6.7 33.3 9.8 7.6 18.3 23.5 12.9 22.7
25.0 62.5 17.5 52.4 39.4 33.3 6.7 46.7 28.3 18.5 41.8 39.2 29.4 39.7
25.0 62.5 25.9 60.8 48.5 54.5 6.7 53.3 34.8 22.8 46.4 45.8 35.9 47.3
0.54 0.75 0.57 0.65 0.75 0.78 0.15 0.49 0.49 0.47 0.48 0.51 0.54 0.60
*The Kanto region includes Tokyo Metropolis and the Ibaraki, Tochigi, Gunma, Saitama, and Chiba Prefectures (See Fig. 1). *The Southern Tohoku region includes the Fukushima, Miyagi, and Yamagata Prefectures (See Fig. 1).
ensembles were made from outputs, each of which had time lags of 120, 96, 72, 48, 24, 24, 48, 72, 96, and 120 min from the initial time, by the continuous calculation (control case) from the period before the initial time. The optimum meteorological field was selected from 11 total ensembles including the control case, as shown in Fig. 3. The analysis period from March 12–31, 2011 was divided into six segments, as displayed in Table 3, so that the various meteorological patterns were generated efficiently by a combination of cases from the ensemble calculation for each time segment. These time segments were divided at the times around midnight and during the period when the plume flowed toward the ocean to reduce the influence of discontinuity in temporal change of the meteorological field on the dispersion calculation. The selection of the optimum case was carried out as follows. First, 11 ensemble calculations of WRF were conducted for the first segments, and the dispersion calculations were carried out for all ensembles to make dispersion databases. The optimum case for the first segment was then selected by applying equation (3) for the dispersion databases. In the analysis of equation (3), only air concentration data of 137Cs (dust sampling [MEXT, 2011a; Ohkura et al., 2012; Furuta et al., 2011; Yamada et al., 2013; Amano et al., 2012; KEK, 2011; METI, 2011;
TEPCO, 2011b] and the SPM data [Oura et al., 2015]) were used for monitoring data vector d, considering the reproducibility of the plume passage at the monitoring point. Second, the control calculation for the second segment was executed by extending the calculation period of the selected case for the first segment, and 10 ensembles were also gener ated from the control case. The dispersion calculations were then carried out, and the optimum case for the second segment was selected in the same manner. These procedures were repeated sequentially for all time segments, and the optimum meteorological field for the whole analysis period was constructed by connecting the meteorological data of opti mum cases for all segments (Table 3). 2.4.3. Optimization method for source term In the second optimization step, the source term was optimized by applying equation (3) for the dispersion database created from the op timum meteorological field for the whole analysis period. In this anal ysis, all available monitoring data were used for the monitoring data vector, d. For 137Cs, air concentration data from dust sampling (47 points, 382 data) [MEXT 2011a; Ohkura et al., 2012; Furuta et al., 2011; Yamada et al., 2013; Amano et al., 2012; KEK 2011; METI 2011; TEPCO 2011b] and the SPM data (100 points, 14,605 data) [Oura et al., 2015;
Fig. 4. Release rate of 137Cs decay corrected at the shutdown time of 14:46 JST on March 11, 2011. Prior (blue line), optimized (open circle with dotted line), and temporally-averaged optimized (red line) release rate. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 7
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Fig. 5. Release rate of 131I decay corrected at the shutdown time of 14:46 JST on March 11, 2011. Prior (blue line), optimized (open circle with dotted line), and temporally-averaged optimized (red line) release rate. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6. Comparison of surface deposition patterns of 137Cs. Upper-right panel exhibits the optimized database from the regional-scale calculation for Domain 2 (The figure is focused to the observation area). Lower-right panel displays the one from the local-scale calculation for Domain 3. The areas of ocean and lakes are white in the calculation maps. Open triangles denote the location of FDNPS. 8
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Fig. 7. Comparison of surface deposition patterns of 131I. Right panel illustrates the result from the local-scale calculation for Domain 3 (The figure is focused to the observation area). The areas of ocean are white in the calculations map. Open triangles denote the location of FDNPS.
Tsuruta et al., 2018], as well as surface deposition data by airborne monitoring (3,523 grid data) [MEXT 2012], and fallout data (13 points, 260 data) [MEXT 2011d] were used. For the SPM data, 6-h average values (0–6, 6–12, 12–18, and 18–24 JST) instead of the original hourly values were employed to reduce discrepancies caused by temporal de viation of the calculated plume passage at the monitoring points, which remained after the optimization of the meteorological calculation. For 131I, the release rate for the total iodine was optimized by assuming constant chemical composition (I2:CH3I:particulate iodine ¼ 2:3:5) based on that 1:1 is used for the gas (I2þCH3I):particulate iodine ratio during most periods, and 2:3 is used for the I2:CH3I ratio by Katata et al. (2015). For the monitoring data vector, d, air concentration data from dust sampling (47 points, 416 data) [MEXT 2011a; Ohkura et al., 2012; Furuta et al., 2011; Yamada et al., 2013; Amano et al., 2012; KEK 2011; METI 2011; TEPCO 2011b], surface deposition data from airborne monitoring (4,126 grid data) [Torii et al., 2013], and fallout data (13 points, 260 data) [MEXT 2011d] were used. In addition to these data, air concentration data of 137Cs from the SPM data [Oura et al., 2015; Tsuruta et al., 2018] for the period of March 12–16, 2011 were also used after converting them to 131I values using 131I/137Cs ratios of air con centrations from dust sampling. The release rates for this period should be optimized by using air concentration data because these had large uncertainties due to the estimation method using air dose rate data in the previous source term estimation [Katata et al., 2012b, 2015]. Additionally, 131I/137Cs ratios of air concentrations from dust sampling after this period are inadequate for use for this purpose because they are affected by the resuspension of deposited iodine. For the plume passage over the northern area of FDNPS from March 12 to 13, 2011, 131I/137Cs ratio of 10 was used based on the air concentration data from dust sampling [METI 2011] and the analysis of body surface contamination levels [Oba et al., 2017]. For the plume passed over the Kanto region (see footnote in Table 5) and Fukushima Prefecture during March 14–15, 2011, 131I/137Cs ratio of 10 was also used based on the air concentration data from dust sampling [MEXT 2011a; Ohkura et al., 2012; Furuta et al., 2011; Yamada et al., 2013; Amano et al., 2012; KEK 2011]. For the plume dispersed over the eastern part of the Kanto region from the night of March 15 to the morning of March 16, 2011, 131I/137Cs ratio of 60 was used for the SPM points in the Kanto region based on the air concen tration data from dust sampling [Ohkura et al., 2012; Furuta et al., 2011; Yamada et al., 2013; Amano et al., 2012; KEK 2011] and 131I/137Cs ratio of 30 was used for one SPM point located 17.5 km south of FDNPS considering the difference in deposition processes of 131I and 137Cs, similarly to the previous study [Katata et al., 2015].
3. Results and discussion 3.1. Optimized source term and dispersion database The optimized source terms of 137Cs and 131I are summarized in Table 4, and the temporal changes of the release rates are demonstrated in Figs. 4 and 5. Although the optimization analysis outputted hourly release rates for these radionuclides, the values were averaged for each release segment with a constant rate according to the prior release rate [Katata et al., 2015; Chino et al., 2016] because the hourly values fluctuated significantly. In the present study, these release rates were decay corrected at the shutdown time of 14:46 JST on March 11, 2011, unlike the release rate at the time when the release occurred in our previous source term estimation [Chino et al., 2011; Katata et al., 2012a, 2012b; Terada et al., 2012, Katata et al., 2015, Chino et al., 2016]. It is because that this type of release rate is convenient for converting release rates of some other radionuclides by applying the radioactivity ratio in the reactor core inventory. Moreover, the treatment of a constant release rate that has been decay corrected is relevant to express the release rate with a constant source condition, in which the ratio of the release amount to the total inventory of each radionuclide in the reactors is constant. Compared to the previous source term [Katata et al., 2015; Chino et al., 2016], release rates of 137Cs decreased by 0.25 times and 0.39 times at the time period corresponding to the wet venting and hydrogen explosion, respectively, at Unit 1 in the afternoon of March 12, 2011. Release rates of 137Cs from the night of March 14 to the early morning of March 15, 2011 decreased, and the peak time shifted to later hours. Release rates of 137Cs also decreased for other periods (in the morning of March 15, from March 18 to 19, on March 20, and from the night of March 21 to March 23, 2011). Release rates of 131I increased 1.8 times for the period corresponding to the wet venting at Unit 1 and decreased by 0.55 times for the period corresponding to the hydrogen explosion at Unit 1 in the afternoon of March 12, 2011. For the release rates of 131I from the night of March 14 to the early morning of March 15, 2011, the peak values at 23:00 JST on March 14 and at 01:00 JST on March 15 increased and decreased, respectively, which were less correlated with the release rate of 137Cs. Relating to the plume with high 131I/137Cs ratios passing the eastern Kanto region in the morning of March 16, the release rate of 131I at 22:00 JST on March 15 increased. Release rates of 131 I decreased for other periods (09:00 to 10:00 JST on March 16, March 18–19, March 20–23, and March 28–29, 2011). The total release amounts from March 12 to March 31, 2011 of 137Cs and 131I are 1.0 � 9
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Fig. 8. Scatter plots of the surface deposition of (a, b) 137Cs and (c) 131I on April 1, 2011. Calculations are the results using the optimized source term for (a) Domain 2 and (b and c) Domain 3. The black solid lines present 1:1 correspondence, and the bands between the dotted lines indicate the areas within a factor of 10.
1016 and 2.1 � 1017 Bq, respectively, as the decay-corrected values at the shutdown time, and 1.0 � 1016 and 1.2 � 1017 Bq, respectively, as the values from the release rate at each release time which area the same as the values in our previous source term estimation. The total amounts of 137Cs and 131I by the present study decreased by 29% and 20%, respectively, in comparison with those by Katata et al. (2015) (137Cs: 1.4 � 1016 Bq, 131I: 1.5 � 1017 Bq). These are mainly due to decreases of release rate during venting and hydrogen explosion at Unit 1 on March 12, and during the period from March 18 to March 19. The main reason of the changes in release rate is that the uncertainty of the estimation results based on the air dose rate in our previous study has been reduced by newly using the observed time series of air concentration by the SPM data during the plume flow to the north from FDNPS. By applying the radioactivity ratio in the reactor core inventory to the release rate of 137Cs and 131I decay corrected at the shutdown time, we calculated the release rates of other major radionuclides and generated source term files of total 131I, 131I chemical species (I2, CH3I, and particulate iodine), 134Cs, 137Cs, and 132Te (Supplementary mate rial). Then, we constructed the spatiotemporal distribution in the air and on the surface (optimized dispersion database) by applying these release rates to equations (1) and (2). This database includes horizontal 2-D distributions of air concentrations in the surface layer and surface deposition (total deposition, dry deposition, wet deposition, and fog deposition) of radionuclides (total 131I, 131I chemical species (I2, CH3I,
and particulate iodine), 134Cs, 137Cs, and 132Te), horizontal wind com ponents, rain intensity, and ground elevation in two calculation domains (local: 190 � 190 km2 area with a 1 km resolution, regional: 570 � 570 km2 area with a 3 km resolution) for hourly output times from 01:00 JST on March 12 to 00:00 JST on April 1, 2011 (480 output times) [research data: optimized dispersion database]. The optimized dispersion data base can be used for comprehensive dose assessment in tandem with the behavioral pattern of evacuees from the FDNPS accident. The database can also provide basic information for understanding atmospheric dispersion mechanisms and environmental impacts after the FDNPS accident and for improving simulation techniques for atmospheric dispersion. 3.2. Comparison of air concentrations By using the optimized dispersion database, simulated air concen trations of 137Cs were compared with measurements from the SPM data [Oura et al., 2015; Tsuruta et al., 2018]. The wind field in meteoro logical calculation was improved by the optimization and the discrep ancies in plume passage time at monitoring points between calculations and measurements were reduced to less than 3 h in the comparison of time series of air concentration. The statistics of comparison in a 6-h average value are summarized in Table 5. These statistics were calcu lated for fractions of samples within a specific area and period regarding 10
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The total deposition amount of 137Cs by the optimized dispersion database in the regional calculation domain (Domain 2) at 00:00 JST on April 1, 2011 was 3.2 � 1015 Bq, and the deposition amount on the land in Domain 2 was 2.1 � 1015 Bq. The latter amount exhibits better reproducibility in comparison with the measured deposition amount of 137 Cs on land from airborne monitoring (Fig. 6), 2.4 � 1015 Bq, than those in Katata et al. (2015), 3.7 � 1015 Bq. The simulation results ob tained from the optimized dispersion database indicate that approxi mately 21% of the total amount of released 137Cs (1.0 � 1016 Bq) was deposited on land in eastern Japan.
Table 6 Statistics of comparison, the percent within a factor of 2 5, and 10 (FA2, FA5, FA10), correlation coefficient (CC), fractional bias (FB), normalized mean square error (NMSE) of137Cs surface deposition between calculations (Katata et al., 2015 and this study) and airborne monitoring data. Higher scores in comparison between Katata et al., (2015) and this study are exhibited in bold letters. “regional” and “local” indicate the comparison between measurements and calculations for Domain 2 and 3, respectively. Radionuclide FA2 (%) Katata et al. (2015) 137 Cs (regional) 41.3 137 Cs (local) 39.2 131 I (local) 52.1 This study 137 Cs (regional) 35.1 137 Cs (local) 32.4 131 I (local) 32.6
FA5 (%)
FA10 (%)
CC
FB
NMSE
78.2 76.3 86.7
91.8 90.4 95.1
0.63 0.53 0.67
0.25 0.19 0.22
31.9 24.7 21.4
66.8 61.1 71.1
80.8 75.5 88.7
0.78 0.56 0.68
¡0.22 ¡0.25 ¡0.55
7.8 14.7 5.2
3.4. Uncertainties of the source term In the present study, release rates of 137Cs and 131I and the ATDM simulation were optimized by applying an objective analysis based on the Bayesian inference method using various measurements (air con centration, surface deposition, and fallout), including newly released hourly air concentrations of 137Cs from the SPM data [Oura et al., 2015; Tsuruta et al., 2018]. Here, we discuss the reduction of uncertainties of release rates in this optimization. The factors for uncertainties of the source term estimation are the reproducibility of the meteorological field, the composition and chemical form of released radionuclides, timing of release rate change, physical processes in dispersion calcula tion (especially deposition processes), errors or representativeness of measured data, etc. In our previous source term estimation [Chino et al., 2011; Katata et al., 2012a, 2012b; Terada et al., 2012, Katata et al., 2015], the air dose rate data were mainly used in method 2 (comparison of observed and calculated spatial patterns of air dose rates by ground-shines) for the early phase of the accident (before March 15, 2011) because air con centration data were limited. Thus, the assumption of radionuclides’ composition and errors in the deposition calculation caused large un certainties for the estimated source term. These uncertainties were removed by using the air concentrations of 137Cs from the SPM data, and uncertainties in the release rates of 137Cs for this period were reduced substantially. The enhanced usage of air concentrations from the SPM data and objective analysis using various measurements is likely to contribute to the improvement of release rates for other periods. The release rates of 131I for the period before March 16, 2011 were also optimized using the SPM data, although air concentration data of 137 Cs were converted to 131I values by applying 131I/137Cs ratios of air concentrations from dust sampling. Even though there were un certainties related to the 131I/137Cs ratios used in the present study, the uncertainties caused by errors in the deposition calculation were removed at least. However, release rates for 131I were optimized by assuming the constant chemical composition (I2:CH3I:particulate iodine ¼ 2:3:5). It is also considered that a much larger fraction of gaseous iodine was discharged to the atmosphere and converted to particulate iodine during transport in the atmosphere. This process was not considered in the optimization and may become a factor of uncertainty. Furthermore, timings of the release rate change were not revised in the optimization, but they remained as they were in the prior release rate [Katata et al., 2015; Chino et al., 2016]. To reduce these uncertainties, new information based on accident progression analysis and elucidation of these mechanisms through experimental and observational studies are necessary.
the plume passage, as well as for total samples, and were compared with those of the simulation results in a previous study [Katata et al., 2015]. In the present study, the reproducibility of air concentrations of 137Cs at SPM monitoring points was improved (FA10 for total samples increased from 35.9% by the previous study to 47.3%), and the scores became higher, especially for the region of north of FDNPS for both periods of March 12–14 (FA10 increased from 25.0% to 62.5%) and March 18–19 (FA10 increased from 6.7% to 53.3%), 2011 and the Kanto region during the period of March 15–16 (FA10 increased from 25.9% to 60.8%), 2011. These area and period are important for dose estimation regarding the behavioral patterns of evacuees, and these improvements lead to the refinement of dose estimation. These good performances were achieved primally through the reduction of discrepancies in the plume passage time by the optimization of the meteorological calculation, and sec ondary by the optimization of the source term using the SPM data. In both optimizations, the SPM data mainly contributed to the improve ment of simulation results. Concerning the other dataset (air concen tration by dust sampling, surface deposition by airborne monitoring, and fallout) used in the optimization of the source term, their contributions to the optimization were small because these data had been used in the previous source term estimation [Katata et al., 2015] and simulations with the prior source term reproduced these data. However, the improvement of the scores for other regions and periods were limited. The performance of our optimization method depends on the variety of ensembles of the meteorological calculation so that one of the ensembles reproduces the actual meteorological conditions. The method that we used in the present study to generate initial conditions of ensembles uses calculation outputs with a time lag around the initial time. Although the method can reproduce a meteorological field to reduce the discrepancy in a temporal change of the meteorological field, it is limited in coping with the discrepancy in a spatial variation. Further improvement of the meteorological calculation is necessary by more effective way in future studies. 3.3. Comparison of surface deposition By using the optimized dispersion database, simulated surface de positions of 137Cs and 131I were compared with measurements from airborne monitoring [MEXT 2012; Torii et al., 2013]. Comparisons of deposition patterns of 137Cs and 131I are displayed in Figs. 6 and 7, respectively. The scatter plots are presented in Fig. 8, and the statistics are summarized in Table 6. Distribution patterns of 137Cs and 131I were well reproduced by the simulation, although overestimation in the southern area of the Miyagi Prefecture, the southeastern area of the Ibaraki Prefecture, and the northeastern area of the Chiba Prefecture, and underestimation in the northern area of the Fukushima Prefecture are existed for 137Cs. Based on the statistical comparison, more than 80% of the calculated values are within a factor of 10. Other statistical scores also demonstrate good reproducibility by the simulations.
4. Conclusions In the present study, the source term and ATDM simulation were optimized by objective analysis based on Bayesian inference using various measurements (air concentration, surface deposition, and fallout), including newly released hourly air concentrations of 137Cs from the SPM data [Oura et al., 2015; Tsuruta et al., 2018]. To apply this analysis to the local-scale atmospheric dispersion simulations, the new optimization method with combination of ensemble meteorological 11
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calculations and the Bayesian inference method was developed. With this method the reproducibility of meteorological field was improved by feeding back comparison results between the dispersion calculations and measurements of radionuclides. We also constructed a spatiotemporal distribution of major radionuclides (131I in the chemical species of I2, CH3I, and particulate iodine, 132Te, 134Cs, and 137Cs) in the air and on the surface (optimized dispersion database) by using the optimized release rates and ATDM simulations. As a result, the total release amounts from March 12 to March 31, 2011 of 137Cs and 131I were 1.0 � 1016 and 1.2 � 1017 Bq, respectively, which were smaller amounts by 29% and 20%, respectively, in com parison with those by a previous study by Katata et al. (2015). Atmo spheric concentrations of 137Cs determined by the optimized dispersion database demonstrated better reproducibility than those of the previous study. The discrepancies in plume passage time at monitoring points between calculations and measurements were reduced to less than 3 h in the comparison of time series of air concentration. FA10 for total sam ples of air concentrations of 137Cs at SPM monitoring points increased from 35.9% by the previous study to 47.3% by the present study, and the scores became higher, especially for the region of north of FDNPS. This area is important for dose estimation regarding the behavioral patterns of evacuees, and these improvements lead to the refinement of dose estimation. These good performances were achieved primally through the reduction of discrepancies in the plume passage time by the opti mization of the meteorological calculation, and secondary by the opti mization of the source term using the SPM data. Contributions by other dataset to the optimization were small because these data were used in the previous study [Katata et al., 2015] and were consistent with sim ulations using the prior source term. The reproducibility of the surface deposition of 137Cs was also improved, particularly for the total depo sition amount. The deposition amount on the land in eastern Japan was 2.1 � 1015 Bq by the present study, and it was close to the amount of 2.4 � 1015 Bq estimated from airborne monitoring, better than the amount of 3.7 � 1015 Bq by the previous study [Katata et al., 2015]. Un certainties in the source term and spatiotemporal distribution of radio nuclides in the environment were reduced by the optimization of meteorological fields using ensemble technique; implementation of measurement of atmospheric concentrations instead of air dose rates for the optimization of release rates, especially during the early stage; and the objective optimization method to reconstruct radionuclides’ distri bution consistent with many environmental measurement data.
Power Station accident. It consists of 960 data files in the NetCDF format, including horizontal 2-D distributions of air concentration in the surface layer and surface deposition (total deposition, dry deposition, wet deposition, and fog deposition) of radionuclides (total 131I, 131I chemical species (I2, CH3I, and particulate iodine), 134Cs, 137Cs, and 132 Te), horizontal wind components, rain intensity, and ground eleva tion in two calculation domains (local: 190 � 190 km2 area with a 1 km resolution, regional: 570 � 570 km2 area with a 3 km resolution) for hourly output times from 16:00 UTC on March 11 (01:00 JST on March 12) to 15:00 UTC on March 31 (00:00 JST on April 1), 2011 (480 output times). This data have been published in Mendeley Data and are avail able at https://doi.org/10.1016/j.jenvrad.2019.106104. References Amano, H., Akiyama, M., Chunlei, B., Kawamura, T., Kishimoto, T., Kuroda, T., Muroi, T., Odaira, T., Ohta, Y., Takeda, K., Watanabe, Y., Morimoto, T., 2012. Radiation measurements in the Chiba metropolitan area and radiological aspects of fallout from the Fukushima Dai-ichi nuclear power plants accident. J. Environ. Radioact. 111, 42–52. Aoyama, M., Tsumune, D., Hamajima, Y., 2013. Distribution of 137Cs and 134Cs in the north Pacific Ocean: impacts of the TEPCO Fukushima-Daiichi NPP accident. J. Radioanal. Nucl. Chem. 296, 535–539. Barker, D., Huang, X.-Y., Liu, Z., Aulign� e, T., Zhang, X., Rugg, S., Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, Y.-R., Henderson, T., Huang, W., Lin, H.-C., Michalakes, J., Rizvi, S., Zhang, X., 2012. The weather research and forecasting model’s community variational/ensemble data assimilation system: WRFDA. Bull. Am. Meteorol. Soc. 93, 831–843. Chino, M., Nakayama, H., Nagai, H., Terada, H., Katata, G., Yamazawa, H., 2011. Preliminary estimation of release amounts of 131I and 137Cs accidently discharged from the Fukushima Daiichi nuclear power plant into atmosphere. J. Nucl. Sci. 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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors express their gratitude to Dr. Masamichi Chino of the National Institutes for Quantum and Radiological Science and Tech nology, Prof. Hiromi Yamazawa of Nagoya University, and Dr. Matthew Hort of the UK Met Office for their helpful comments and suggestions. This study was conducted in the framework funded and commissioned by the Ministry of Environment of Japan. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvrad.2019.106104. Appendix B. Research data The database was generated by the atmospheric dispersion simula tions using the optimized source term of the Fukushima Daiichi Nuclear 12
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